Removing unnecessary information (noise) from observed information (information corrupted by noise and so forth) in which unnecessary information (noise) is mixed in with desired information (a desired signal), and extracting only desired information, is an important technology in the fields of speech and radio communications, imaging, attitude control, recognition, industrial/welfare/medical robotics, and the like, and has been the subject of considerable research and development in recent years.
For example, a method whereby a single microphone is used and a method whereby a microphone array comprising a plurality of microphones is used have been proposed as heretofore known noise suppression methods in the speech field.
However, with a method that uses a microphone array, microphones at least equal in number to the number of noise sources are necessary, and therefore the number of microphones inevitably increases in proportion to an increase in the number of sound sources, and the cost increases. There are also cases in which practical application is difficult, such as when there is a limit to the number of microphones that can be installed in communication products that are continually becoming smaller in size, such as mobile phones, or when controlling differences in the characteristics of the microphones. Consequently, the development of a noise suppression method that uses a single microphone currently represents the mainstream.
The following are known as conventional noise suppression method algorithms using only a single microphone.
An ANC (adaptive noise canceller) algorithm described in Non-Patent Document 1 reduces a noise signal by employing the periodicity of a speech signal.
A noise suppression algorithm based on linear prediction is described in Non-Patent Document 2. This, algorithm does not require the pitch estimation required by ANC described in Non-Patent Document 1, or prior knowledge concerning a noise power spectrum or noise average direction.
Separately from the above algorithms, a noise suppression algorithm based on a Kalman filter is proposed in Non-Patent Document 3. This algorithm models a speech signal autoregressive (AR) system from an observed signal. Furthermore, this algorithm estimates an AR system parameter (hereinafter “AR coefficient”), and executes noise suppression based on a Kalman filter using the estimated AR coefficient.
Most Kalman filter-based algorithms normally operate in two stages. That is to say, this kind of algorithm first estimates an AR coefficient, and then performs noise suppression based on a Kalman filter using the estimated AR coefficient.    Non-Patent Document 1: J. R. Deller, J. G. Proakis, J. H. L. Hansen, “Discrete-Time Processing of Speech signals,” Macmillan Press, 1993    Non-Patent Document 2: A. Kawamura, K. Fujii, Y. Itoh and Y. Fukui, “A Noise Reduction Method Based on Linear Prediction Analysis,” IEICE Trans. Fundamentals, vol. J85-A, no. 4, pp. 415-423, May 2002    Non-Patent Document 3: W. Kim and H. Ko, “Noise Variance Estimation for Kalman Filtering of Noise Speech,” IEICE Trans. Inf. & syst., vol. E84-D, no. 1, pp. 155-160, January 2001    Non-Patent Document 4: N. Tanabe, T. Inoue, K. Sueyoshi, T. Furukawa, H. Kubota, H. Matsue, and S. Tsujii, “Robust Noise Suppression Algorithm using Kalman Filter Theory with Colored Driving Source,” IEICE Technical Report, EA2007-125, pp. 79-84, March 2008